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The Lancet. Digital Health Aug 2022Wearable activity trackers offer an appealing, low-cost tool to address physical inactivity. This systematic review of systematic reviews and meta-analyses (umbrella... (Review)
Review
Wearable activity trackers offer an appealing, low-cost tool to address physical inactivity. This systematic review of systematic reviews and meta-analyses (umbrella review) aimed to examine the effectiveness of activity trackers for improving physical activity and related physiological and psychosocial outcomes in clinical and non-clinical populations. Seven databases (Embase, MEDLINE, Ovid Emcare, Scopus, SPORTDiscus, the Cochrane Library, and Web of Science) were searched from database inception to April 8, 2021. Systematic reviews of primary studies using activity trackers as interventions and reporting physical activity, physiological, or psychosocial outcomes were eligible for inclusion. In total, 39 systematic reviews and meta-analyses were identified, reporting results from 163 992 participants spanning all age groups, from both healthy and clinical populations. Taken together, the meta-analyses suggested activity trackers improved physical activity (standardised mean difference [SMD] 0·3-0·6), body composition (SMD 0·7-2·0), and fitness (SMD 0·3), equating to approximately 1800 extra steps per day, 40 min per day more walking, and reductions of approximately 1 kg in bodyweight. Effects for other physiological (blood pressure, cholesterol, and glycosylated haemoglobin) and psychosocial (quality of life and pain) outcomes were typically small and often non-significant. Activity trackers appear to be effective at increasing physical activity in a variety of age groups and clinical and non-clinical populations. The benefit is clinically important and is sustained over time. Based on the studies evaluated, there is sufficient evidence to recommend the use of activity trackers.
Topics: Exercise; Fitness Trackers; Humans; Quality of Life; Systematic Reviews as Topic
PubMed: 35868813
DOI: 10.1016/S2589-7500(22)00111-X -
The Lancet. Digital Health Dec 2021Excessive use of digital smart devices, including smartphones and tablet computers, could be a risk factor for myopia. We aimed to review the literature on the... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Excessive use of digital smart devices, including smartphones and tablet computers, could be a risk factor for myopia. We aimed to review the literature on the association between digital smart device use and myopia.
METHODS
In this systematic review and meta-analysis we searched MEDLINE and Embase, and manually searched reference lists for primary research articles investigating smart device (ie, smartphones and tablets) exposure and myopia in children and young adults (aged 3 months to 33 years) from database inception to June 2 (MEDLINE) and June 3 (Embase), 2020. We included studies that investigated myopia-related outcomes of prevalent or incident myopia, myopia progression rate, axial length, or spherical equivalent. Studies were excluded if they were reviews or case reports, did not investigate myopia-related outcomes, or did not investigate risk factors for myopia. Bias was assessed with the Joanna Briggs Institute Critical Appraisal Checklists for analytical cross-sectional and cohort studies. We categorised studies as follows: category one studies investigated smart device use independently; category two studies investigated smart device use in combination with computer use; and category three studies investigated smart device use with other near-vision tasks that were not screen-based. We extracted unadjusted and adjusted odds ratios (ORs), β coefficients, prevalence ratios, Spearman's correlation coefficients, and p values for associations between screen time and incident or prevalent myopia. We did a meta-analysis of the association between screen time and prevalent or incident myopia for category one articles alone and for category one and two articles combined. Random-effects models were used when study heterogeneity was high (I>50%) and fixed-effects models were used when heterogeneity was low (I≤50%).
FINDINGS
3325 articles were identified, of which 33 were included in the systematic review and 11 were included in the meta-analysis. Four (40%) of ten category one articles, eight (80%) of ten category two articles, and all 13 category three articles used objective measures to identify myopia (refraction), whereas the remaining studies used questionnaires to identify myopia. Screen exposure was measured by use of questionnaires in all studies, with one also measuring device-recorded network data consumption. Associations between screen exposure and prevalent or incident myopia, an increased myopic spherical equivalent, and longer axial length were reported in five (50%) category one and six (60%) category two articles. Smart device screen time alone (OR 1·26 [95% CI 1·00-1·60]; I=77%) or in combination with computer use (1·77 [1·28-2·45]; I=87%) was significantly associated with myopia. The most common sources of risk of bias were that all 33 studies did not include reliable measures of screen time, seven (21%) did not objectively measure myopia, and nine (27%) did not identify or adjust for confounders in the analysis. The high heterogeneity between studies included in the meta-analysis resulted from variability in sample size (range 155-19 934 participants), the mean age of participants (3-16 years), the standard error of the estimated odds of prevalent or incident myopia (0·02-2·21), and the use of continuous (six [55%] of 11) versus categorical (five [46%]) screen time variables INTERPRETATION: Smart device exposure might be associated with an increased risk of myopia. Research with objective measures of screen time and myopia-related outcomes that investigates smart device exposure as an independent risk factor is required.
FUNDING
None.
Topics: Adolescent; Adult; Cell Phone Use; Child; Child, Preschool; Computers; Female; Humans; Infant; Infant, Newborn; Male; Myopia; Risk Factors; Screen Time; Smartphone; Social Media; Vision, Ocular; Young Adult
PubMed: 34625399
DOI: 10.1016/S2589-7500(21)00135-7 -
The Lancet. Digital Health Jun 2022Skin cancers occur commonly worldwide. The prognosis and disease burden are highly dependent on the cancer type and disease stage at diagnosis. We systematically... (Review)
Review
Skin cancers occur commonly worldwide. The prognosis and disease burden are highly dependent on the cancer type and disease stage at diagnosis. We systematically reviewed studies on artificial intelligence and machine learning (AI/ML) algorithms that aim to facilitate the early diagnosis of skin cancers, focusing on their application in primary and community care settings. We searched MEDLINE, Embase, Scopus, and Web of Science (from Jan 1, 2000, to Aug 9, 2021) for all studies providing evidence on applying AI/ML algorithms to the early diagnosis of skin cancer, including all study designs and languages. The primary outcome was diagnostic accuracy of the algorithms for skin cancers. The secondary outcomes included an overview of AI/ML methods, evaluation approaches, cost-effectiveness, and acceptability to patients and clinicians. We identified 14 224 studies. Only two studies used data from clinical settings with a low prevalence of skin cancers. We reported data from all 272 studies that could be relevant in primary care. The primary outcomes showed reasonable mean diagnostic accuracy for melanoma (89·5% [range 59·7-100%]), squamous cell carcinoma (85·3% [71·0-97·8%]), and basal cell carcinoma (87·6% [70·0-99·7%]). The secondary outcomes showed a heterogeneity of AI/ML methods and study designs, with high amounts of incomplete reporting (eg, patient demographics and methods of data collection). Few studies used data on populations with a low prevalence of skin cancers to train and test their algorithms; therefore, the widespread adoption into community and primary care practice cannot currently be recommended until efficacy in these populations is shown. We did not identify any health economic, patient, or clinician acceptability data for any of the included studies. We propose a methodological checklist for use in the development of new AI/ML algorithms to detect skin cancer, to facilitate their design, evaluation, and implementation.
Topics: Algorithms; Artificial Intelligence; Early Detection of Cancer; Humans; Machine Learning; Primary Health Care; Skin Neoplasms
PubMed: 35623799
DOI: 10.1016/S2589-7500(22)00023-1 -
The Lancet. Digital Health Sep 2022Telemedicine has been increasingly integrated into chronic disease management through remote patient monitoring and consultation, particularly during the COVID-19... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Telemedicine has been increasingly integrated into chronic disease management through remote patient monitoring and consultation, particularly during the COVID-19 pandemic. We did a systematic review and meta-analysis of studies reporting effectiveness of telemedicine interventions for the management of patients with cardiovascular conditions.
METHODS
In this systematic review and meta-analysis, we searched PubMed, Scopus, and Cochrane Library from database inception to Jan 18, 2021. We included randomised controlled trials and observational or cohort studies that evaluated the effects of a telemedicine intervention on cardiovascular outcomes for people either at risk (primary prevention) of cardiovascular disease or with established (secondary prevention) cardiovascular disease, and, for the meta-analysis, we included studies that evaluated the effects of a telemedicine intervention on cardiovascular outcomes and risk factors. We excluded studies if there was no clear telemedicine intervention described or if cardiovascular or risk factor outcomes were not clearly reported in relation to the intervention. Two reviewers independently assessed and extracted data from trials and observational and cohort studies using a standardised template. Our primary outcome was cardiovascular-related mortality. We evaluated study quality using Cochrane risk-of-bias and Newcastle-Ottawa scales. The systematic review and the meta-analysis protocol was registered with PROSPERO (CRD42021221010) and the Malaysian National Medical Research Register (NMRR-20-2471-57236).
FINDINGS
72 studies, including 127 869 participants, met eligibility criteria, with 34 studies included in meta-analysis (n=13 269 with 6620 [50%] receiving telemedicine). Combined remote monitoring and consultation for patients with heart failure was associated with a reduced risk of cardiovascular-related mortality (risk ratio [RR] 0·83 [95% CI 0·70 to 0·99]; p=0·036) and hospitalisation for a cardiovascular cause (0·71 [0·58 to 0·87]; p=0·0002), mostly in studies with short-term follow-up. There was no effect of telemedicine on all-cause hospitalisation (1·02 [0·94 to 1·10]; p=0·71) or mortality (0·90 [0·77 to 1·06]; p=0·23) in these groups, and no benefits were observed with remote consultation in isolation. Small reductions were observed for systolic blood pressure (mean difference -3·59 [95% CI -5·35 to -1·83] mm Hg; p<0·0001) by remote monitoring and consultation in secondary prevention populations. Small reductions were also observed in body-mass index (mean difference -0·38 [-0·66 to -0·11] kg/m; p=0·0064) by remote consultation in primary prevention settings.
INTERPRETATION
Telemedicine including both remote disease monitoring and consultation might reduce short-term cardiovascular-related hospitalisation and mortality risk among patients with heart failure. Future research should evaluate the sustained effects of telemedicine interventions.
FUNDING
The British Heart Foundation.
Topics: COVID-19; Cardiovascular Diseases; Heart Failure; Humans; Pandemics; Telemedicine
PubMed: 36028290
DOI: 10.1016/S2589-7500(22)00124-8 -
NPJ Digital Medicine Apr 2021Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic... (Review)
Review
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC's ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC's ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC's ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.
PubMed: 33828217
DOI: 10.1038/s41746-021-00438-z -
The Lancet. Digital Health Mar 2023Digital health interventions have shown promising results for the management of type 2 diabetes, but a comparison of the effectiveness and implementation of the... (Meta-Analysis)
Meta-Analysis
Effectiveness, reach, uptake, and feasibility of digital health interventions for adults with type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials.
BACKGROUND
Digital health interventions have shown promising results for the management of type 2 diabetes, but a comparison of the effectiveness and implementation of the different modes is not currently available. Therefore, this study aimed to compare the effectiveness of SMS, smartphone application, and website-based interventions on improving glycaemia in adults with type 2 diabetes and report on their reach, uptake, and feasibility.
METHODS
In this systematic review and meta-analysis, we searched CINAHL, Cochrane Central, Embase, MEDLINE, and PsycInfo on May 25, 2022, for randomised controlled trials (RCTs) that examined the effectiveness of digital health interventions in reducing glycated haemoglobin A (HbA) in adults with type 2 diabetes, published in English from Jan 1, 2009. Screening was carried out using Covidence, and data were extracted following Cochrane's guidelines. The primary endpoint assessed was the change in the mean (and 95% CI) plasma concentration of HbA at 3 months or more. Cochrane risk of bias 2 was used to assess risk of bias. Data on reach, uptake, and feasibility were summarised narratively and data on HbA reduction were synthesised in a meta-analysis. Grading of Recommendations, Assessment, Development, and Evaluation criteria was used to evaluate the level of evidence. The study was registered with PROSPERO, CRD42021247845.
FINDINGS
Of the 3236 records identified, 56 RCTs from 24 regions (n=11 486 participants), were included in the narrative synthesis, and 26 studies (n=4546 participants) in the meta-analysis. 20 studies used SMS as the primary mode of delivery of the digital health intervention, 25 used smartphone applications, and 11 implemented interventions via websites. Smartphone application interventions reported higher reach compared with SMS and website-based interventions, but website-based interventions reported higher uptake compared with SMS and smartphone application interventions. Effective interventions, in general, included people with greater severity of their condition at baseline (ie, higher HbA) and administration of a higher dose intensity of the intervention, such as more frequent use of smartphone applications. Overall, digital health intervention group participants had a -0·30 (95% CI -0·42 to -0·19) percentage point greater reduction in HbA, compared with control group participants. The difference in HbA reduction between groups was statistically significant when interventions were delivered through smartphone applications (-0·42% [-0·63 to -0·20]) and via SMS (-0·37% [-0·57 to -0·17]), but not when delivered via websites (-0·09% [-0·64 to 0·46]). Due to the considerable heterogeneity between included studies, the level of evidence was moderate overall.
INTERPRETATION
Smartphone application and SMS interventions, but not website-based interventions, were associated with better glycaemic control. However, the studies' heterogeneity should be recognised. Considering that both smartphone application and SMS interventions are effective for diabetes management, clinicians should consider factors such as reach, uptake, patient preference, and context of the intervention when deciding on the mode of delivery of the intervention. Nine in ten people worldwide own a feature phone and can receive SMS and four in five people have access to a smartphone, with numerous smartphone applications being available for diabetes management. Clinicians should familiarise themselves with this modality of programme delivery and encourage people with type 2 diabetes to use evidence-based applications for improving their self-management of diabetes. Future research needs to describe in detail the mediators and moderators of the effectiveness and implementation of SMS and smartphone application interventions, such as the optimal dose, frequency, timing, user interface, and communication mode to both further improve their effectiveness and to increase their reach, uptake, and feasibility.
FUNDING
EU's Horizon 2020 Research and Innovation Programme.
Topics: Humans; Adult; Feasibility Studies; Diabetes Mellitus, Type 2; Smartphone; Mobile Applications; Randomized Controlled Trials as Topic
PubMed: 36828606
DOI: 10.1016/S2589-7500(22)00233-3 -
NPJ Digital Medicine Jun 2023Chatbots (also known as conversational agents and virtual assistants) offer the potential to deliver healthcare in an efficient, appealing and personalised manner. The... (Review)
Review
Chatbots (also known as conversational agents and virtual assistants) offer the potential to deliver healthcare in an efficient, appealing and personalised manner. The purpose of this systematic review and meta-analysis was to evaluate the efficacy of chatbot interventions designed to improve physical activity, diet and sleep. Electronic databases were searched for randomised and non-randomised controlled trials, and pre-post trials that evaluated chatbot interventions targeting physical activity, diet and/or sleep, published before 1 September 2022. Outcomes were total physical activity, steps, moderate-to-vigorous physical activity (MVPA), fruit and vegetable consumption, sleep quality and sleep duration. Standardised mean differences (SMD) were calculated to compare intervention effects. Subgroup analyses were conducted to assess chatbot type, intervention type, duration, output and use of artificial intelligence. Risk of bias was assessed using the Effective Public Health Practice Project Quality Assessment tool. Nineteen trials were included. Sample sizes ranged between 25-958, and mean participant age ranged between 9-71 years. Most interventions (n = 15, 79%) targeted physical activity, and most trials had a low-quality rating (n = 14, 74%). Meta-analysis results showed significant effects (all p < 0.05) of chatbots for increasing total physical activity (SMD = 0.28 [95% CI = 0.16, 0.40]), daily steps (SMD = 0.28 [95% CI = 0.17, 0.39]), MVPA (SMD = 0.53 [95% CI = 0.24, 0.83]), fruit and vegetable consumption (SMD = 0.59 [95% CI = 0.25, 0.93]), sleep duration (SMD = 0.44 [95% CI = 0.32, 0.55]) and sleep quality (SMD = 0.50 [95% CI = 0.09, 0.90]). Subgroup analyses showed that text-based, and artificial intelligence chatbots were more efficacious than speech/voice chatbots for fruit and vegetable consumption, and multicomponent interventions were more efficacious than chatbot-only interventions for sleep duration and sleep quality (all p < 0.05). Findings from this systematic review and meta-analysis indicate that chatbot interventions are efficacious for increasing physical activity, fruit and vegetable consumption, sleep duration and sleep quality. Chatbot interventions were efficacious across a range of populations and age groups, with both short- and longer-term interventions, and chatbot only and multicomponent interventions being efficacious.
PubMed: 37353578
DOI: 10.1038/s41746-023-00856-1 -
Journal of Clinical Medicine May 2023Perioperative disorders of neurocognitive function are a set of heterogeneous conditions, which include transient post-operative delirium (POD) and more prolonged... (Review)
Review
Perioperative disorders of neurocognitive function are a set of heterogeneous conditions, which include transient post-operative delirium (POD) and more prolonged post-operative cognitive dysfunction (POCD). Since the number of annually performed surgical procedures is growing, we should identify which type of anesthesia is safer for preserving neurocognitive function. The purpose of this study was to compare the effect of general anesthesia (GA) and regional anesthesia (RA) in patients undergoing surgical procedures under general anesthesia and regional anesthesia. We searched for randomized controlled studies, which studied post-operative cognitive outcomes after general and regional anesthesia in the adult patient population. Thirteen articles with 3633 patients: the RA group consisted of 1823 patients, and the GA group of 1810 patients, who were selected for meta-analysis. The overall effect of the model shows no difference between these two groups in terms of risk for post-operative delirium. The result is insensitive to the exclusion of any study. There was no difference between RA and GA in terms of post-operative cognitive dysfunction. There was no statistically significant difference between GA and RA in the incidence of POD. There was no statistically significant difference in the incidence of POCD per-protocol analysis, psychomotor/attention tests (preoperative/baseline, post-operative), memory tests (postoperatively, follow up), mini-mental state examination score 24 h postoperatively, post-operative reaction time three months postoperatively, controlled oral word association test, and digit copying test. There were no differences in the incidence of POCD in general and regional anesthesia at one week postoperatively, three months postoperatively, or total events (one week or three months). The incidence of post-operative mortality also did not differ between two groups.
PubMed: 37240655
DOI: 10.3390/jcm12103549 -
The Lancet. Digital Health Jul 2022Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced...
Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study.
BACKGROUND
Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications.
METHODS
We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC).
FINDINGS
In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683-0·717]).
INTERPRETATION
In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required.
FUNDING
British Journal of Surgery Society.
Topics: Adult; COVID-19; Cohort Studies; Humans; Pandemics; Postoperative Complications; Prognosis; Prospective Studies
PubMed: 35750401
DOI: 10.1016/S2589-7500(22)00069-3 -
Sleep Medicine: X Dec 2023Insomnia is a common disease, and the application of various types of sleeping pills for cognitive impairment is controversial, especially as different doses can lead to... (Review)
Review
BACKGROUND
Insomnia is a common disease, and the application of various types of sleeping pills for cognitive impairment is controversial, especially as different doses can lead to different effects. Therefore, it is necessary to evaluate the cognitive impairment caused by different sleeping pills to provide a theoretical basis for guiding clinicians in the selection of medication regimens.
OBJECTIVE
To evaluate whether various different doses (low, medium and high) of anti-insomnia drugs, such as the dual-orexin receptor antagonist (DORA), zopiclone, eszopiclone and zolpidem, induce cognitive impairment.
METHODS
The PubMed, Embase, Scopus, Cochrane Library, and Google Scholar databases were searched from inception to September 20th, 2022 for keywords in randomized controlled trials (RCTs) to evaluate the therapeutic effects of DORA, eszopiclone, zopiclone and zolpidem on sleep and cognitive function. The primary outcomes were indicators related to cognitive characteristics, including scores on the Digit Symbol Substitution Test (DSST) and daytime alertness. The secondary outcomes were the indicators associated with sleep and adverse events. Continuous variables were expressed as the standard mean difference (SMD). Data were obtained through GetData 2.26 and analyzed by Stata v.15.0.
RESULTS
A total of 8702 subjects were included in 29 studies. Eszopiclone significantly increased the daytime alertness score (SMD = 3.00, 95 % CI: 1.86 to 4.13) compared with the placebo, and eszopiclone significantly increased the daytime alertness score (SMD = 4.21, 95 % CI: 1.65 to 6.77; SMD = 3.95, 95 % CI: 1.38 to 6.51; SMD = 3.26, 95 % CI: 0.38 to 6.15; and SMD = 3.23, 95 % CI: 0.34 to 6.11) compared with zolpidem, zolpidem, DORA, and eszopiclone, respectively. Compared with the placebo, zopiclone, zolpidem, and eszopiclone, DORA significantly increased the TST (SMD = 2.39, 95 % CI: 1.11 to 3.67; SMD = 6.00, 95 % CI: 2.73 to 9.27; SMD = 1.89, 95 % CI: 0.90 to 2.88; and SMD = 1.70, 95 % CI: 0.42 to 2.99, respectively).
CONCLUSION
We recommend DORA as the best intervention for insomnia because it was highly effective in inducing and maintaining sleep without impairing cognition. Although zolpidem had a more pronounced effect on sleep maintenance, this drug is better for short-term use. Eszopiclone and zopiclone improved sleep, but their cognitive effects have yet to be verified.
PubMed: 38149178
DOI: 10.1016/j.sleepx.2023.100094